Partially Observable Markov Decision Processes (POMDP)

A Partially Observable Markov Decision Process (POMDP) is a framework used to make decisions in situations where the true state of the environment is not fully known. It models how an agent perceives uncertain information through noisy observations and chooses actions to maximize expected rewards over time. POMDPs consider probabilities for different states and observations, allowing the agent to update its beliefs and make informed decisions despite incomplete or ambiguous information. This approach is useful in complex real-world scenarios like robotics, medical diagnosis, and autonomous systems, where perfect knowledge isn't possible.